Wrist Motion Capture System

A computer vision-based system for tracking and analyzing wrist range of motion (ROM) using colored markers. Designed for physical therapy and rehabilitation tracking with real-time feedback and comprehensive progress analytics.

Demo

Real-time wrist motion tracking and progress analytics

Features

🎯 Real-Time Motion Tracking

• Color Marker Detection: Tracks orange, yellow, pink, and blue markers
• Angle Calculation: Precise measurement of radial and ulnar deviation angles
• Live Feedback: Instant "GREAT", "TOO FAR", or "TOO LITTLE" guidance
• Dual Arm Support: Configurable for left or right wrist

📊 Progress Analytics

• Session Tracking: Duration, movement count, and accuracy percentage
• Personal Bests: Maximum achieved angles for both movement types
• Trend Analysis: Shows improvement, decline, or stable performance patterns
• Historical Data: Session summaries saved for long-term progress tracking

🎮 Interactive Controls

• Manual Recording: Press SPACEBAR to capture specific angles
• Reset Function: Press R to clear recorded measurements
• Easy Exit: Press ESC to save session and exit

💾 Data Storage

• results.csv: Continuous logging of all movements with timestamps
• recorded_angles.csv: Manually recorded angle measurements
• progress_history.json: Comprehensive session analytics and trends

Installation

Prerequisites

• Python 3.7 or higher
• Webcam/camera for motion capture
• Colored markers (orange, yellow, pink, blue)

Install Dependencies

pip install numpy opencv-python pysine statistics

Quick Start

git clone https://github.com/yourusername/wrist-motion-capture.git
cd wrist-motion-capture
python mocap.py

Usage

Setup

1. Attach Markers: Secure colored markers to your wrist device
2. Position Camera: Ensure clear view of all markers
3. Configure Lighting: Good lighting improves marker detection

Initial Configuration

When you run the program, you'll be prompted to enter:

• Arm Selection: Left (L) or Right (R) wrist
• ROM Targets: Minimum and maximum angles for radial deviation
• ROM Targets: Minimum and maximum angles for ulnar deviation

During Session

• Live Display: Three windows show camera feed, marker tracking, and progress
• Real-time Feedback: Visual and text feedback on movement quality
• Manual Recording: Press SPACEBAR when you achieve a target angle
• Reset Recordings: Press R to clear manual recordings
• Exit: Press ESC to save session and close

Controls Reference

Key	Action
ESC	Exit and save session
SPACEBAR	Record current angle
R	Reset recorded angles

Understanding the Display

Main Windows

1. Camera Feed: Raw camera input
2. Angle Tracking: Processed view with marker detection and lines
3. Progress Display: Statistics and feedback overlay

Progress Information

• Current Angle: Live angle measurement with movement type
• Session Stats: Total movements and accuracy percentage
• Personal Bests: Maximum achieved angles
• Recorded Angles: Count and last manually recorded value
• Trend Indicator: Performance trajectory (↗ ↘ →)

Data Analysis

Session Summary

At the end of each session, you'll see:

=== SESSION SUMMARY ===
Duration: 5.2 minutes
Total movements: 150
Accuracy: 78.5%
Average angle: 42.3°
Radial movements: 75 (max: 65.2°)
Ulnar movements: 75 (max: 58.7°)
Manual recordings: 8 angles saved
Recorded angles: [45.2, 52.1, 38.9, 61.3, 44.7, 59.8, 47.2, 55.6]
=======================

Data Files

• results.csv: Timestamp, angle, movement type, feedback for every movement
• recorded_angles.csv: Manually recorded measurements with context
• progress_history.json: Session analytics for tracking improvement over time

Adding Demo Media

To include your demo photo/video:

For Screenshots

1. Create a demo folder in your project directory
2. Add your screenshot as demo/screenshot.png
3. The README will automatically reference it

For Videos

1. Add your video file as demo/demo_video.mp4
2. Alternative: Upload to YouTube/Vimeo and replace the link:

   [Watch Demo Video](https://youtu.be/your-video-id)

File Structure

wrist-motion-capture/
├── mocap.py                 # Main application
├── README.md               # This file
├── demo/                   # Demo media folder
│   ├── screenshot.png      # Interface screenshot
│   └── demo_video.mp4      # Sample session video
├── results.csv             # Generated: Movement data
├── recorded_angles.csv     # Generated: Manual recordings
└── progress_history.json   # Generated: Session summaries

Troubleshooting

Common Issues

• Camera not detected: Ensure webcam is connected and not used by other apps
• Markers not tracking: Check lighting and color contrast
• ESC not working: Click on camera window first, then press ESC
• Import errors: Run pip install command for missing packages

Marker Detection Tips

• Use bright, distinct colors (orange, yellow, pink, blue)
• Ensure good lighting without shadows
• Keep markers visible to camera at all times
• Clean marker surfaces for better color detection

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with OpenCV for computer vision capabilities
• Designed for physical therapy and rehabilitation applications
• Supports research in motion analysis and recovery tracking

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Note: This system is for educational and research purposes. Consult healthcare professionals for medical applications.